01 How We Work 02 Our Framework 03 Real estate 04 Where to Start 05 About 06 Case Studies 07 Start the Conversation
Real estate Brokerages · Real estate developers

Real estate loses sales and time between the first inquiry and the signed contract.

In brokerages, the leaks sit in the inquiry, the listing, the showing and the offer. For real estate developers, they run from the first contact with a buyer to the handover of the unit. Framelab finds each one and closes it end to end: diagnosis, design, the automations and AI we build, implementation, training, and monitoring of the KPIs until adoption holds.

100
New brokers onboarded in one month · St. Petersburg
2 weeks
To peak performance for new brokers, instead of six months · St. Petersburg
2x
Annual revenue · USA and LATAM
22%
More deals, same traffic · Miami
The journey

Where each leak shows up.

Choose the type of company, then tap a point on the line: what the leak looks like, what it costs, what we do about it and how we measure it.

Brokerages: from the inquiry to the contract

Self-check

How many of these leaks are in your operation?

Answer yes or no. Nothing is sent anywhere and there is no form at the end.

  1. Do you know how many of last week’s inquiries didn’t get a response the same day?

  2. Does a property go live only once every document is in the file?

  3. When a client moves to another agent, do they have to explain what they are looking for again?

  4. Is the way your best agents work written down and used to train new ones?

  5. Does a new agent take months to reach full production?

  6. Does leadership review every open deal and its next step at least once a week?

KPIs

What we measure in real estate.

We take a baseline for each KPI in the diagnosis and report against it through implementation and monitoring.

  1. 01

    Time to first contact

    From the moment an inquiry arrives to the first reply the client gets.

  2. 02

    Share of inquiries answered the same day

    How many get a first contact before the day closes.

  3. 03

    Inquiry-to-showing and showing-to-reservation conversion

    Where demand turns into visits, and visits into reservations.

  4. 04

    Days from listing agreement to published listing

    How long a signed property waits before buyers can see it.

  5. 05

    Time until a new agent is productive

    How long a new agent takes to perform like the best on the team.

  6. 06

    Up-to-date unit inventory

    The share of units whose status matches across sales, brokers and the website.

Real estate cases

Four real estate operations, before and after.

Every engagement is under NDA, so only the location and the number of brokers are named. Each case links to its full PDF.

Real Estate St. Petersburg, Florida, USA
2 weeks To peak performance for new brokers, down from six months

St. Petersburg, Florida · 100-broker brokerage

Context

A 100-broker brokerage hired us after losing two deals, one worth USD 1.1 million and another worth USD 1.3 million. They thought they knew where their operational leaks were but couldn’t confirm it. They had already adopted several AI tools, and none was delivering results because none was being used properly.

The problem

  • Client communication was scattered across text messages and different messaging apps.
  • The CRM was generic, not built for real estate, and used halfway and without rules.
  • Leadership found out a deal was at risk only after it was lost.
  • New brokers took about six months to reach peak performance.

What we did

  • We interviewed every level and traced real deals end to end, including the two lost ones, to find where follow-up broke.
  • We brought everything into a single platform, ClickUp: the CRM, templates and client messaging, with one record per client and a mandatory next step on every deal.
  • We audited the AI tools they already had, removed the ones that weren’t working and built the ones that were into the process.
  • We built a dashboard with alerts for deals at risk.
  • We documented the method of the top brokers and built the onboarding program from it.
  • We trained the whole team and monitored adoption.

Quick wins

  • All client messaging in one place, with templates for each stage.
  • Every deal with an owner and a visible next step.
  • Alerts when a deal goes days without activity.

Results

  • Revenue grew more than 100% in one year.
  • 100 new brokers onboarded in one month.
  • New brokers reached peak performance in two weeks instead of six months.
Real Estate USA and LATAM
2x Annual Revenue

USA and LATAM · Brokerage operating in two markets

Context

A brokerage working across two markets that ran on the people in it, not on the systems around them. That works while a company is small. When it tries to grow, the absence of systems becomes the ceiling.

The problem

  • Every broker had their own way of following up with clients, managing listings and closing deals.
  • A large share of the team’s time went to administrative tasks that required no real judgment.
  • Critical information was not being captured anywhere.
  • Pipeline visibility existed only in each broker’s head.

What we did

  • We mapped how properties, clients and contracts were actually managed in both markets, including the informal workarounds built up over years.
  • We built a framework that brought consistency to the client relationship without losing the personal touch that makes real estate work.
  • We redesigned and shortened the contract processes and automated reporting.
  • We made pipeline visibility shared and real time.
  • We trained the whole team on the new tools and on using AI for reporting, communications and analysis, and stayed through implementation and the first months of adoption.

Quick wins

  • Reporting automated.
  • One shared, real-time view of the pipeline.
  • A shorter, redesigned contract process.

Results

  • Revenue doubled.
  • Time spent on manual administrative tasks was cut in half.
  • The contract closing cycle got 40% shorter.
  • The company took on 30% more clients without growing the team in proportion.
  • 75% of the operational dependence on individual brokers was removed.
  • 15 hours freed per person per week, 55% of processes automated and 58% lower operational costs.
Real Estate Miami, Florida, USA
22% More Deals, Same Traffic

Miami, Florida · 50-broker office of a multi-city US brokerage

Context

The Miami office of a brokerage with offices in several US cities, with a team of 50 brokers, was generating plenty of demand and losing a large share of it on the way.

The problem

  • Portal inquiries arrived by email and on the front desk’s WhatsApp, and were forwarded by hand from chat to chat.
  • Nobody owned the path from inquiry to broker, to showing, to offer.
  • Each handoff dropped something: the client’s budget, the property they had asked about, what had already been promised.
  • More than a third of inquiries got no first contact on the day they arrived, and clients had to tell their story again at every step.

What we did

  • We followed real inquiries from the first message to the signed contract, and talked with the front desk, the brokers, the transaction coordinator and the owners.
  • We found the six points where the work changed hands without an owner or a deadline.
  • We gave every handoff one owner and one maximum time, and built a single record per client with a mandatory next step.
  • We set up an automatic assignment that notifies the broker on duty by message.
  • We put AI into the handoffs: Claude summarizes each conversation and the client’s needs before every handoff, and drafts the first reply for the broker to review and send.
  • We trained the whole team on the new flow and on working with AI, and stayed through the first months to adjust it with the numbers in hand.

Quick wins

  • Every inquiry assigned automatically to the broker on duty.
  • One record per client, with a mandatory next step.
  • A named owner and a maximum time for every handoff.

Results

  • First response time dropped from six hours to 25 minutes.
  • Inquiries with no follow-up fell from 35% to under 8%.
  • 22% more deals closed from the same traffic, without adding brokers.
  • The path from inquiry to contract got 35% shorter.
  • Each broker recovered 12 hours a week.
Real Estate Paraguay
7 days To Spot a Stalled Deal

Paraguay · 8-broker agency

Context

A sales-focused agency in Paraguay with eight brokers found out about its problems too late.

The problem

  • Each broker kept their own spreadsheet.
  • The owners saw the real state of the pipeline only when the month closed, after two days of copying figures by hand.
  • A negotiation that had gone cold was usually discovered when the client had already signed with another agency.
  • Decisions on prices, advertising and which brokers needed support were made on information that was weeks old.

What we did

  • We sat with each broker and with the owners to see how a deal was really followed from first inquiry to deed, and which unwritten signals told them a deal was in trouble.
  • We turned those signals into four indicators: inquiries, visits, reservations and days without movement per deal.
  • We built a shared board where every deal lives in one place, with one rule: any deal with no activity for ten days turns red and alerts the broker and the owners.
  • We put AI into the weekly review: every Monday Claude reads the board and writes the summary of what moved, what stalled and a suggested next step for each deal. Brokers and owners decide what to do.
  • We trained the brokers and the owners on the board and on using AI to prepare their follow-ups, and stayed through the first quarter to adjust the rules.

Quick wins

  • Every deal in one shared board.
  • A red flag and an alert after ten days without activity.
  • A monthly report no longer copied by hand.

Results

  • Stalled deals are detected at seven days instead of thirty or more.
  • The monthly report went from two days to twenty minutes, and the owners review the pipeline every week instead of every month.
  • Four negotiations recovered in the first quarter that would otherwise have been lost.
  • 18% more reservations.
  • Each person recovered 10 hours a week.
How we work

Renewable Intelligence, our own methodology.

People and AI work together at every step. Framelab does all of it, end to end: the diagnosis, the design, building the automations and the AI, implementation, training, and monitoring until adoption holds.

01: Discovery

A first conversation about how the brokerage or the developer sells today, and whether there is a fit.

02: Diagnosis and audit

We talk to every level of the team and follow real inquiries and deals end to end. AI works through the CRM records and message histories with us, and every KPI gets its baseline.

The entry point

Every engagement starts with the diagnostic pilot: the diagnosis and audit run on your own operation, with a baseline for each KPI and a map of where sales and time are lost. From there, you decide on implementation.

03: Custom framework

Renewable Intelligence adapted to your operation: the process for each stage, the automation plan, and where AI works alongside the team.

04: Implementation and training

We build the automations and the AI into the tools the team already uses, set up the records, dashboards and alerts, and train every broker and manager.

05: Monitoring

We track the KPIs against the baseline and adjust until adoption holds.

The First Step

Find the leaks before the next lost deal.

A 30-minute call about how your operation sells today, or a line to mina@framelab.works.